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Learning Data Mining with Python, - Second Edition

You're reading from  Learning Data Mining with Python, - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Pages 358 pages
Edition 2nd Edition
Languages
Concepts

Table of Contents (20) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Data Mining 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

Feature extraction


Extracting features is one of the most critical tasks in data mining, and it generally affects your end result more than the choice of data mining algorithm. Unfortunately, there are no hard and fast rules for choosing features that will result in high-performance data mining. The choice of features determines the model that you are using to represent your data.

Note

Model creation is where the science of data mining becomes more of an art and why automated methods of performing data mining (there are several methods of this type) focus on algorithm choice and not model creation. Creating good models relies on intuition, domain expertise, data mining experience, trial and error, and sometimes a little luck.

Representing reality in models

Given what we have done so far in the book, it is easy to forget that the reason we are performing data mining is to affect real world objects, not just manipulating a matrix of values. Not all datasets are presented in terms of features....

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